Many of the ml validation articles that have 101-10000 training datapoints [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]} use svm [1]}, [2]}, [4]}, [5]}, [6]}, [7]}, [8]}, [10]}, [12]}, [13]}, [15]}, [18]}, [19]}, [20]}, [22]}, [23]}. There are also other examples such as ensemble [5]}, ann [19]}, rf [7]}, dt [15]}, [19]}, rfe [22]}, lasso [3]}, crfs [13]}, [6]}, [5]}, doe [4]}, lr [7]}, [15]}, [19]}, pr [19]}, pls [5]}, matrix-based recommender [17]}, smote [5]}, knn [5]}, [15]}, [19]}, and krr [8]} approaches. Of the "other" ml articles, only [3]}, [9]}, [11]}, [14]}, [16]}, [17]}, [21]} are not already included in the svm group, indicating that svm is often combined or compared with other methods. Most of the svm articles described here employ a rbf kernel, imposing smooth, Gaussian behavior on the predicted properties. We now share examples of experimental [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [10]}, [12]}, [13]}, [14]}, [15]}, [16]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]} and computational [1]}, [8]}, [9]}, [17]} validation articles, addressing svm/ad (sec:101-10000:svm-ad), svm/crfs (sec:101-10000:svm-crfs), general svm (sec:101-10000:svm-general), and non-svm (sec:101-10000:non-svm).
